Care Electronic Medical Record Research Articles

A systematic review on how primary care electronic medical record data have been used for antimicrobial stewardship

Abstract Objective: This systematic review aims to synthesize evidence from current literature to describe how Electronic Medical Record (EMR) primary care data have been used for antimicrobial stewardship activities internationally. Design: Systematic literature review. Methods: We searched Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, Embase, Scopus, and Web of Science from January 1, 2013 to September 23, 2023 to retrieve studies that included concepts of “antimicrobial stewardship,” “primary care,” and “electronic medical records.” We used narrative synthesis to classify and interpret results. Data were grouped and tabulated by similar themes and concepts, including strengths, facilitators, barriers, and limitations for antimicrobial stewardship. Results: A total of 265 articles were identified from the initial search, of which 34 full-text articles from 10 countries met all criteria and were included in the review. Six categories of EMR data use were identified from the studies, these were for: assessing antimicrobial prescribing quality, measuring the effectiveness of an intervention, analyzing antimicrobial prescribing trends, assessing patient and provider characteristics in prescribing, evaluating novel tools or measures, and measuring specific conditions and outcomes. Facilitators of use of EMR data were generally well-described across the studies reviewed; however, barriers were not. Barriers described were centered around EMR system design and technical challenges in data extraction. Completeness of EMR data was the most consistently described limitation. Conclusions: Our study highlights the range of uses of EMR data in supporting AMS in primary care internationally, and its strengths, facilitators, and barriers to use.

Development and validation of a rheumatoid arthritis case definition: a machine learning approach using data from primary care electronic medical records

BackgroundRheumatoid Arthritis (RA) is a chronic inflammatory disease that is primarily diagnosed and managed by rheumatologists; however, it is often primary care providers who first encounter RA-related symptoms. This study developed and validated a case definition for RA using national surveillance data in primary care settings.MethodsThis cross-sectional validation study used structured electronic medical record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Based on the reference set generated by EMR reviews by five experts, three machine learning steps: ‘bag-of-words’ approach to feature generation, feature reduction using a feature importance measure coupled with recursive feature elimination and clustering, and classification using tree-based methods (Decision Tree, Random Forest, and Extreme Gradient Boosting). The three tree-based algorithms were compared to identify the procedure that generated the optimal evaluation metrics. Nested cross-validation was used to allow evaluation and comparison and tuning of models simultaneously.ResultsOf 1.3 million patients from seven Canadian provinces, 5,600 people aged 19 + were randomly selected. The optimal algorithm for selecting RA cases was generated by the XGBoost classification method. Based on feature importance scores for features in the XGBoost output, a human-readable case definition was created, where RA cases are identified when there are at least 2 occurrences of text “rheumatoid” in any billing, encounter diagnosis, or health condition table of the patient chart. The final case definition had sensitivity of 81.6% (95% CI, 75.6–86.4), specificity of 98.0% (95% CI, 97.4–98.5), positive predicted value of 76.3% (95% CI, 70.1–81.5), and negative predicted value of 98.6% (95% CI, 98.0-98.6).ConclusionA case definition for RA in using primary care EMR data was developed based off the XGBoost algorithm. With high validity metrics, this case definition is expected to be a reliable tool for future epidemiological research and surveillance investigating the management of RA in CPCSSN dataset.

Implementation of a novel linkage of primary care electronic medical record data with hospital data in South Eastern Ontario

Implementation of a novel linkage of primary care electronic medical record data with hospital data in South Eastern Ontario

Exploring Prediabetes Pathways Using Explainable AI on Data from Electronic Medical Records.

This study leverages data from a Canadian database of primary care Electronic Medical Records to develop machine learning models predicting type 2 diabetes mellitus (T2D), prediabetes, or normoglycemia. These models are used as a basis for extracting counterfactual explanations and derive personalized changes in biomarkers to prevent T2D onset, particularly in the still reversible prediabetic state. The models achieve satisfactory performance. Furthermore, feature importance analysis underscores the significance of fasting blood sugar and glycated hemoglobin, while counterfactuals explanations emphasize the centrality of keeping body mass index and cholesterol indicators within or close to the clinically desirable ranges. This research highlights the potential of machine learning and counterfactual explanations in guiding preventive interventions that may help slow down the progression from prediabetes to T2D on an individual basis, eventually fostering a recovery from prediabetes to a normoglycemic state.

An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design.

Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, they have now become a standard part of care. EMRs have the potential to be further optimized with the introduction of artificial intelligence (AI). There has yet to be a widespread exploration of the use of AI in primary health care and how clinicians envision AI use to encourage further uptake. The primary objective of this research is to understand if the user-centered design approach, rooted in contextual design, can lead to an increased likelihood of adoption of an AI-enabled encounter module embedded in a primary care EMR. In this study, we use human factor models and the technology acceptance model to understand the results. To accomplish this, a partnership has been established with an industry partner, TELUS Health, to use their EMR, the collaborative health record. The overall intention is to understand how to improve the user experience by using user-centered design to inform how AI should be embedded in an EMR encounter. Given this intention, a user-centered approach will be used to accomplish it. The approach of user-centered design requires qualitative interviewing to gain a clear understanding of users' approaches, intentions, and other key insights to inform the design process. A total of 5 phases have been designed for this study. As of March 2024, a total of 14 primary care clinician participants have been recruited and interviewed. First-cycle coding of all qualitative data results is being conducted to inform redesign considerations. Some limitations need to be acknowledged related to the approach of this study. There is a lack of market maturity of AI-enabled EMR encounters in primary care, requiring research to take place through scenario-based interviews. However, this participant group will still help inform design considerations for this tool. This study is targeted for completion in the late fall of 2024. DERR1-10.2196/54365.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

Prevalence and management of dyslipidemia in primary care practices in Canada.

To estimate the prevalence of dyslipidemia and to describe its management in Canadian primary care. Retrospective cohort study using primary care electronic medical record data. Canada. Adults aged 40 years or older who saw a Canadian Primary Care Sentinel Surveillance Network contributor between January 1, 2018, and December 31, 2019. Presence or absence of dyslipidemia as identified by a validated case definition and the treatment status of patients identified as having dyslipidemia based on having been prescribed a lipid-lowering agent (LLA). In total, 50.0% of the 773,081 patients 40 years of age or older who had had a primary care visit in 2018 or 2019 were identified as having dyslipidemia. Dyslipidemia was more prevalent in patients 65 or older (61.5%), in males (56.7%) versus females (44.7%), and in those living in urban areas (50.0%) versus rural areas (45.2%). In patients with documented dyslipidemia, 42.8% had evidence of treatment with an LLA. Stratifying patients by Framingham risk score revealed that those in the high-risk category were more likely to have been prescribed an LLA (65.0%) compared with those in the intermediate-risk group (48.7%) or the low-risk group (22.8%). The strongest determinants of receiving LLA treatment for dyslipidemia include sex, with males being 1.95 times more likely to have been treated compared with females (95% CI 1.91 to 1.98; P<.0001); and body mass index, with those with obesity having a significantly increased likelihood of being treated with an LLA (adjusted odds ratio of 1.36, 95% CI 1.32 to 1.41; P<.0001). This study provides an updated look at the prevalence and treatment of dyslipidemia among Canadians. Half of patients aged 40 years or older have dyslipidemia, with an even higher prevalence observed among adults aged 65 years or older, males, and those with obesity or other chronic conditions. There are still gaps in treatment among those with documented dyslipidemia, principally among those calculated to have high or intermediate Framingham risk scores. Particular attention should also be paid to those at higher risk for not receiving treatment, including female patients and those within normal body mass index ranges.

Identifying patients with diagnosed cirrhosis in administrative health databases: a validation study.

Case ascertainment algorithms were developed and validated to identify people living with cirrhosis in administrative health data in Manitoba, Canada using primary care electronic medical records (EMR) to define the reference standards. We linked provincial administrative health data to primary care EMR data. The validation cohort included 116,675 Manitobans aged >18 years with at least one primary care visit between April 1998 and March 2015. Hospital records, physician billing claims, vital statistics, and prescription drug data were used to develop and test 93 case-finding algorithms. A validated case definition for primary care EMR data was the reference standard. We estimated sensitivity, specificity, positive and negative predictive values (PPV, NPV), Youden's index, area under the receiver operative curve, and their 95% confidence intervals (CIs). A total of 116,675 people were in the validation cohort. The prevalence of cirrhosis was 1.4% (n = 1593). Algorithm sensitivity estimates ranged from 32.5% (95% CI 32.2-32.8) to 68.3% (95% CI 68.0-68.9) and PPV from 17.4% (95% CI 17.1-17.6) to 23.4% (95% CI 23.1-23.6). Specificity (95.5-98.2) and NPV (approximately 99%) were high for all algorithms. The algorithms had slightly higher sensitivity estimates among men compared with women, and individuals aged ≥45 years compared to those aged 18-44 years. Cirrhosis algorithms applied to administrative health data had moderate validity when a validated case definition for primary care EMRs was the reference standard. This study provides algorithms for identifying diagnosed cirrhosis cases for population-based research and surveillance studies.

Validating pertussis data measures using electronic medical record data in Ontario, Canada 1986–2016

BackgroundPertussis is a reportable disease in many countries, but ascertainment bias has limited data accuracy. This study aims to validate pertussis data measures using a reference standard that incorporates different suspected case severities, allowing for the impact of case severity on accuracy and detection to be explored. MethodsWe evaluated 25 pertussis detection algorithms in a primary care electronic medical record database between January 1, 1986 and December 30, 2016. We estimated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We used sensitivity analyses to explore areas of uncertainty and evaluated reasons for lack of detection. ResultsThe algorithm including all data measures achieved the highest sensitivity at 20.6%. Sensitivity increased to 100% after reclassifying symptom-only cases as non-cases, but the PPV remained low. Age at first episode was significantly associated with detection in half of the tested scenarios, and false negatives often had some history of immunization. ConclusionsSensitivity improved by reclassifying symptom-only cases but remained low unless multiple data sources were used. Results demonstrate a trade-off between PPV and sensitivity. EMRs can enhance detection through patient history and clinical note data. It is essential to improve case identification of older individuals with vaccination history to reduce ascertainment bias.

Validation of adult asthma case definitions for primary care sentinel surveillance.

Most asthma diagnoses and patient care take place in primary care settings. Electronic medical records (EMRs) offer an opportunity to utilize technology to improve asthma diagnosis and care. The purpose of this study was to create and validate separate case definitions for suspected and confirmed asthma in primary care EMRs, to enable surveillance, benchmarking, and quality improvement in primary care settings. The objective of this study was to develop a case definition for suspected and confirmed asthma for use in a primary care sentinel surveillance system. A single chart abstractor conducted a manual audit of 776 randomly selected patient charts from an academic primary care practice EMR in Kingston, Ontario. Following the single chart abstractor classification, a consensus on chart classification as "not asthma", "suspected asthma", or "confirmed asthma" was achieved between the abstractor, a family physician, and a respirologist using Canadian Thoracic Society (CTS) criteria. Case definition algorithms based on billing codes, clinical data elements and medications were applied to the site's Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data for the same charts and compared to abstractor classifications to determine each algorithm's measurement properties. The prevalence of suspected and confirmed asthma were 7.3% (n = 54) and 2.4% (n = 18), respectively. None of the proposed case definitions could differentiate between suspected and confirmed asthma. One algorithm consisting of billing, clinical, and medication elements had the highest Youden's Index for either suspected or confirmed asthma. The algorithm had a sensitivity of 81%, a specificity of 96%, positive predictive value of 71%, negative predictive value of 98%, and a Youden's Index of 0.77 for combined suspected or confirmed asthma cases. An EMR case definition for suspected or confirmed adult asthma has been validated for use in CPCSSN. Implementation of this case definition will enable the development of a surveillance electronic tool (eTool) for adult asthma that can foster quality improvement.

Empowering the Role of Nurse Triage in Radiation Oncology - A Single Center Analysis.

Hours spent on the increasing volume of electronic medical record (EMR) messages and phone calls is a significant contributor to clinician burnout1. Within radiation oncology, nurses often serve a critical team role in patient education and EMR and phone call response. With the goal of maintaining high quality care and rapid EMR and phone response, our clinic utilizes a rotating triage nurse position; patterns of practice and team member perspectives are investigated. A retrospective chart review was conducted analyzing EMR messages and phone calls to the RO department between Sept-Dec 2021. Messages were identified for theme, author of message recorded (patient, family, provider), number of message iterations, and message pathway. Separately, an online survey was administered via REDCap to physicians (50% response rate), nurses (57% response rate), and radiation therapist (60% response rate) to assess nurse triage efficacy and efficiency. Four of the most common message themes were scheduling (26%), medications (21%), side effects/symptoms (15%), and treatment decision making (13%). Only 12% of messages were resolved by the RO triage nurse without further message forwarding. The average number of message iterations by healthcare personnel was 2.93 with mode of 2 (i.e., RN -> patient -> MD is a 2 message iteration). Across all themes physicians concluded 67% of the routed messages and nursing concluded 29%. By theme, physicians concluded 95% of treatment questions, 77% of medication questions, 69% of side effect/symptom questions, and 57% of scheduling questions. Survey results indicated average satisfaction with 'effectiveness of triage' was 3 (max 5), perceived triage nurse preparedness to resolve encounters was 3 (max 5), and perception of the overall EMR triage program by physicians was 2.2 (max 5). Staff survey established clear opportunity to improve effectiveness of RN triage through better alignment of nurse and physician expectations and improving nurse preparedness. Two-thirds of patient messages were ultimately concluded by physician response whereas 12% were resolved by nurse triage alone. Preparing nursing staff about common patient messages should increase autonomy to resolve patient queries and may improve work satisfaction for all providers through enhanced multidisciplinary collaboration and empowering individuals to practice at top of his/her license.

Assessing Primary Care Blood Pressure Documentation for Hypertension Management During the COVID-19 Pandemic by Patient and Provider Groups

BackgroundPrimary care electronic medical record (EMR) data can be used to identify, manage, and screen hypertension cases. However, this approach relies on completeness and accessibility of documented blood pressure (BP) values. With the large switch to virtual care due to the COVID-19 pandemic, we assessed BP documentation in primary care EMRs during the pandemic, across patient and physician groups. MethodsHypertension-related visits were identified during the pre-pandemic (January 2017 to February 2020) and pandemic (March 2020 to December 2021) periods from a primary care EMR database in Ontario, Canada. Clustered logistic regression models were used to analyze the relationship of physician and patient characteristics with an outcome variable of documented BP. A chart review of 3200 hypertension visits without a BP recorded in structured data fields was conducted to determine if BP was recorded in progress notes. ResultsPre-pandemic, 75.7% of hypertension-related visits (113,966 of 150,511) had a BP recorded in structured documentation, but this significantly decreased to 36.4% (26,660 of 73,239) during the pandemic (odds ratio [OR] = 0.18, 95% confidence interval [CI]: 0.18-0.19). For virtual visits, 14.3% (6357 of 44,572) had a documented BP, vs 74.0% (20,056 of 27,089) for in-person visits. Chart review found that 55.9% of hypertension visits had no associated BP in structured documentation, but did have a BP recorded in the progress note. Male providers, compared to female providers, were less likely to record BPs pre-pandemic (OR = 0.45, 95% CI: 0.32-0.63) and during the pandemic, for both virtual visits (OR = 0.48, 95% CI: 0.32-0.71) and in-person visits (OR = 0.46, 95% CI: 0.33-0.64). ConclusionsBP documented in primary care EMRs declined during the pandemic, most likely due to high rates of virtual visits impacting hypertension detection and management.

Real-World Effectiveness in Hypertension and Hyperlipidemia Collaborative Management between Pharmacies and Primary Care in Portugal: A Multicenter Pragmatic Controlled Trial (USFarmácia®).

There is evidence of the efficacy of collaborative health interventions with pharmacies and primary care providers but little of its real-world effectiveness. We aimed to assess the effectiveness and discuss the design and challenges of hypertension and hyperlipidemia management between pharmacies and primary care providers using real-world data exchange between providers and experimental bundled payment. This was a pragmatic, quasi-experimental controlled trial. We collected patient-level data from primary care prescription claims and Electronic Medical Record databases, a pharmacy claims database, and patient telephone surveys at several time points. The primary outcomes were changes in blood pressure and total cholesterol. We used matched controls with difference-in-differences estimators in a Generalized Linear Model (GLM) and controlled interrupted time series (CITS). We collected additional data for economic and qualitative studies. A total of 6 Primary Care Units, 20 pharmacies, and 203 patients entered the study. We were not able to observe significant differences in the effect of intervention vs. control. We experienced challenges that required creative strategies. This real-world trial was not able to show effectiveness, likely due to limitations in the primary care technology which affected the sample size. It offers, however, valuable lessons on methods, strategies, and data sources, paving the way for more real-world effectiveness trials to advance value-based healthcare.

Effects of the COVID-19 Pandemic on Primary Health Care for Chronic Conditions in Canada: Protocol for a Retrospective Pre-Post Study Using National Practice-Based Research Network Data.

Since the COVID-19 pandemic began, there have been concerns that interruptions to the health care system may have led to changes in primary care, especially for care of chronic conditions such as diabetes and heart failure. Such changes may have longer term implications for population health. This study aims to describe the impacts of the COVID-19 pandemic on indicators of primary care access, comprehensiveness, and appropriateness among adult patients, as well as on specific indictors of chronic conditions. Additionally, this study aims to determine whether any identified changes were associated with patient sociodemographic characteristics and multimorbidity. This is a retrospective, single-arm, pre-post study using Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data. CPCSSN is a research network supported by a primary care electronic medical record database, comprising over 1500 physicians and nearly 2 million patients. We are examining changes in care (eg, frequency of contacts, laboratory tests and investigations, referrals, medications prescribed, etc) among adults. We will also examine indicators specific to evidence-based recommendations for care in patients with diabetes and those with heart failure. We will compare rates of outcomes during key periods of the pandemic between March 13, 2020, and December 31, 2022, with equal time periods before the pandemic. Differences will be examined among specific subgroups of adults, including by decade of age, number of comorbidities, and socioeconomic status. Regression models appropriate to outcome distributions will be used to estimate changes, adjusting for potential confounders. This analysis is part of a mixed-methods study with a qualitative component investigating how patients with diabetes with or without concurrent heart failure perceived the impact of the pandemic on access to primary care and health care-related decisions. This study was approved by the Hamilton Integrated Research Ethics Board (14782-C). The start date of this study was October 5, 2022, and the prospective end date is January 31, 2024. As of May 2023, the study cohort (n=875,934) is defined, data cleaning is complete, and exploratory analyses have begun. Extended analyses using 2022 data are planned once the new data becomes available. We will disseminate results through peer-reviewed publications and academic conference, as well as creating evidence briefs, infographics, and a video for policy maker and patient audiences. This study will investigate whether the COVID-19 pandemic has resulted in changes in the provision of primary care in Canada and whether these potential changes have led to gaps in care. This study will also identify patient-level characteristics associated with changes in care patterns across the COVID-19 pandemic. Indicators specific to chronic conditions, namely diabetes and heart failure, will also be explored to determine whether there were changes in care of these conditions. ClinicalTrials.gov NCT05813652; https://clinicaltrials.gov/ct2/show/NCT05813652. RR1-10.2196/49131.

DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.

Heatlhcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable, and reliable machine learning models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient, safe and high-quality manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher-created models into a widely used electronic medical record system. We discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within electronic medical record software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact. We demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating 12 machine learning models trained using electronic medical record data that predict laboratory diagnostic results, triggered by clinician button-clicks in Stanford Health Care's electronic medical record. Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. When possible, we recommend using prospectively estimated performance measures during silent trials to make final go decisions for model deployment. Machine learning applications in healthcare are extensively researched, but successful translations to the bedside are rare. By describing DEPLOYR, we aim to inform machine learning deployment best practices and help bridge the model implementation gap.

Sex differences in the clinical presentation of early psychosis in a primary care setting.

Primary care is an important part of the help-seeking pathway for young people experiencing early psychosis, but sex differences in clinical presentation in these settings are unexplored. We aimed to identify sex differences in clinical presentation to primary care services in the 1-year period prior to a first diagnosis of psychotic disorder. We identified first-onset cases of non-affective psychotic disorder over a 10-year period (2005-2015) using health administrative data linked with electronic medical records (EMRs) from primary care (n = 465). Detailed information on encounters in the year prior to first diagnosis was abstracted, including psychiatric symptoms, other relevant behaviours, and diagnoses recorded by the family physician (FP). We used modified Poisson regression models to examine sex differences in the signs, symptoms, and diagnoses recorded by the FP, adjusting for various clinical and sociodemographic factors. Positive symptoms (PR = 0.76, 95%CI: 0.58, 0.98) and substance use (PR = 0.54, 95%CI: 0.40, 0.72) were less prevalent in the medical records of women. Visits by women were more likely to be assigned a diagnosis of depression or anxiety (PR = 1.18, 95%CI: 1.00, 1.38), personality disorder (PR = 5.49, 95%CI: 1.22, 24.62), psychological distress (PR = 11.29, 95%CI: 1.23, 103.91), and other mental or behavioral disorders (PR = 3.49, 95%CI: 1.14, 10.66) and less likely to be assigned a diagnosis of addiction (PR = 0.33, 95%CI: 0.13, 0.87). We identified evidence of sex differences in the clinical presentation of early psychosis and recorded diagnoses in the primary care EMR. Further research is needed to better understand sex differences in clinical presentation in the primary care context, which can facilitate better understanding, detection, and intervention for first-episode psychotic disorders.

Stratified primary care for adults with musculoskeletal pain: the STarT MSK research programme including RCTs

Stratified primary care for adults with musculoskeletal pain: the STarT MSK research programme including RCTs

REVEAL-CKD: PREVALENCE OF UNDIAGNOSED STAGE 3 CHRONIC KIDNEY DISEASE IN PATIENTS WITH DIAGNOSED HYPERTENSION IN CANADA

Objective: Hypertension is a risk factor for and complication of chronic kidney disease (CKD), a progressive condition affecting 11.1% of the global population. Early recognition and diagnosis of CKD is crucial for effective management to slow disease progression and improve patient outcomes. This REVEAL-CKD study assessed the prevalence of, and factors associated with, undiagnosed stage 3 CKD in patients with hypertension. Design and method: REVEAL-CKD is an observational study using secondary data from electronic medical records (EMR) and claims data. Data were extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database, that consists of primary care EMR data from approximately 1,500 general practice clinicians across eight provinces and one territory of Canada. The study cohort included patients aged at least 18 years with two consecutive estimated glomerular filtration rate (eGFR) values 30-59 mL/min/1.73m2 recorded 91-730 days apart between 2015-2021. The date of the second qualifying eGFR was the index date. Included patients were required to have hypertension diagnosis code before index. Patients with no CKD diagnosis code before and up to 6 months after index were considered undiagnosed. This study was granted approval by the CPCSSN data access committee (project number: 2022SRE147). Results: Of the 45,914 patients meeting the study inclusion criteria for stage 3 CKD, median age at index date was 77 years (interquartile range [IQR]: 69-84 years) and 19,597 (42.7%) were male. Of these patients, 32,192 (70.1%) also had an existing diagnosis of hypertension. Among patients meeting the criteria for stage 3 CKD and having a diagnosis of hypertension there were 30,523 patients, (94.8%) who did not have a recorded diagnosis of CKD up to 6 months after index. Of these patients, only one fifth was diagnosed with CKD after 4 years. Conclusions: A large proportion of patients in Canada with hypertension and stage 3 CKD do not have a recorded CKD diagnosis, and there is substantial diagnostic delay despite clinical evidence. This likely results in patients not receiving guideline-directed management to reduce the risk of disease progression and adverse clinical outcomes that could have been avoided by early detection of CKD.

Validation of diagnosis of acute myocardial infarction and stroke in electronic medical records: a primary care cross-sectional study in Madrid, Spain (the e-MADVEVA Study)

ObjectivesTo validate the diagnoses of acute myocardial infarction (AMI) and stroke recorded in electronic medical records (EMR) and to estimate the population prevalence of both diseases in people aged ≥18...

Validation of a primary care electronic medical records case definition for eczema: retrospective cross-sectional study

BackgroundTo validate case definitions for eczema using primary care Electronic Medical Record (EMR) data from the Canadian Primary Care Sentential Surveillance Network (CPCSSN).MethodsThis study used EMR data from 1,574 primary care providers in seven Canadian provinces, representing 689,301 patients. Using a subset of patient records seven medical students or family medicine residents created a reference set of 1,772 patients. A total of 23 clinician-informed case definitions were validated against the reference. We assessed agreement using sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and overall accuracy. The case definitions with the best agreement statistics were deployed to estimate the prevalence of eczema in the CPCSSN.ResultsCase definition 1 had the highest SE (92.1%,85.0-96.5) but a lower SP (88.5%,86.7–90.1) and PPV (36.6%,33.1–40.3). Case definition 7 was the most specific case definition with a SP (99.8%, 99.4–100) and PPV (84.2%,61.2–94.7) but low SE (15.8%,9.3–24.5). Case definition 17 had a SE (75.3%, 65.7–83.3), SP (93.8%, 91.5–94.3) and PPV 43.7% (38.3–49.2). When we applied the most specific and most sensitive case definitions, we estimate the prevalence of eczema to be between 0.8 and 15.1%. Case definition 17 suggests an eczema prevalence estimate of 8.2% (8.08–8.21%).ConclusionsWe validated EMR-based eczema case definitions to estimate the prevalence of clinician-documented eczema. Future studies may choose to apply one or more of these definitions’ dependent on their studies objectives to inform disease surveillance as well as explore burden of illness or interventions related to eczema care in Canada.