Primary Care Electronic Medical Record Research Articles

Temporal Trend in Young-Onset Type 2 Diabetes-Macrovascular and Mortality Risk: Study of U.K. Primary Care Electronic Medical Records.

To evaluate temporal prevalence trend, cardiometabolic risk factors, and the risk of atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality (ACM) in incident young- and usual-onset type 2 diabetes. From the U.K. primary care database, 370,854 people with a new diagnosis of type 2 diabetes from 2000 to 2017 were identified. Analyses were conducted by age-group (18-39, 40-49, 50-59, 60-69, 70-79 years) and high-/low-risk status without history of ASCVD at diagnosis, with subjects with two or more of current smoking, high systolic blood pressure, high LDL cholesterol (LDL-C), or chronic kidney disease classified as high risk. The proportion of people aged <50 years at diagnosis increased during 2000-2010 and then stabilized. The incidence rates of ASCVD and ACM declined in people aged ≥50 years but did not decrease in people <50 years. Compared with people aged ≥50 years, those aged 18-39 years at diagnosis had a higher proportion of obesity (71% obese) and higher HbA1c (8.6%), and 71% had high LDL-C, while only 18% were on cardioprotective therapy. Although 2% in this age-group had ASCVD at diagnosis, 23% were identified as high risk. In the 18-39-year age-group, the adjusted average years to ASCVD/ACM in high-risk individuals (9.1 years [95% CI 8.2-10.0]/9.3 years [8.1-10.4]) were similar to the years in those with low risk (10.0 years [9.5-10.5]/10.5 years [9.7-11.2]). However, individuals aged ≥50 years with high risk were likely to experience an ASCVD event 1.5-2 years earlier and death 1.1-1.5 years earlier compared with low-risk groups (P < 0.01). Unlike usual-onset, young-onset type 2 diabetes has similar cardiovascular and mortality risk irrespective of cardiometabolic risk factor status at diagnosis. The guidelines on the management of young-onset type 2 diabetes for intensive risk factor management and cardioprotective therapies need to be urgently reevaluated through prospective studies.

Supporting Asthma and COPD Best Practices in Ontario through Primary Care Electronic Medical Records.

Asthma and chronic obstructive pulmonary disease (COPD) are two of the most common chronic respiratory diseases. Electronic medical records (EMRs) are increasingly being used as a means to support chronic disease management and enable best practices. This article summarizes the state of asthma and COPD care and electronic solutions in Ontario, highlighting the work done to date for asthma and COPD data standards. Lessons learned and future considerations for the use of EMRs and related electronic solutions to support chronic disease management are discussed.

Validation of a type 1 diabetes algorithm using electronic medical records and administrative healthcare data to study the population incidence and prevalence of type 1 diabetes in Ontario, Canada

IntroductionWe aimed to develop algorithms distinguishing type 1 diabetes (T1D) from type 2 diabetes in adults ≥18 years old using primary care electronic medical record (EMRPC) and administrative healthcare data...

Illustrating the patient journey through the care continuum: Leveraging structured primary care electronic medical record (EMR) data in Ontario, Canada using chronic obstructive pulmonary disease as a case study

BackgroundAlthough barriers exist to secondary use of primary care electronic medical record (EMR) data, the Alliance for Healthier Communities (the Alliance) in Ontario, Canada has successfully created one of the largest structured primary care EMR datasets in Canada. In 2018, the Alliance and the Canadian Institute for Health Information (CIHI), an organization that provides comparable and actionable data to accelerate improvements in health across Canada, entered into a partnership to share EMR data. In this paper, we describe (i) the processes that enabled the collection of structured EMR data by the Alliance; (ii) how CIHI connected with the Alliance to share data and assess its quality; and, (iii) demonstrate the value of linking structured EMR data to administrative acute care data in illustrating the patient journey through the care continuum, using COPD as a case study. MethodsCIHI and the Alliance entered into a formal data sharing agreement that enabled the sharing of linkable structured EMR data by the Alliance’s 75 community health centres (CHCs) with CIHI. By leveraging the Alliance’s Business Intelligence Reporting Tool, 3 years of EMR data containing patient-level clinical data were shared with CIHI. CIHI assessed the EMR data quality using its Data Source Assessment Tool to ensure fitness for analytical use. By linking the patient level EMR records with hospital records (CIHI’s discharge abstract database (DAD) and the national ambulatory care reporting system (NACRS)), we examined aspects of COPD patient management in primary care and followed their journey through the health care continuum, including follow-up in primary care after hospital discharge. ResultsAlliance EMR data representing approximately 570,000 patients and 8.5 million primary care encounters between April 1, 2015 and March 31, 2018 were shared with CIHI. A data quality assessment, centered on completeness and concordance, confirmed that the data was fit for analytical purposes. Overall, 13,023 enrolled primary care patients were identified as having COPD, representing an overall crude prevalence of 8.7%. The average age of COPD patients was 64 years and equally affected males and females. Patients were most likely to have completed high school education or equivalent, speak English, live alone, and have a household income less than $15,000. They most commonly had between 10–19 primary care encounters a year with a range of providers where they most commonly sought services for health advice/ instructions, to discuss their treatment plans and for medication renewals. By linking the EMR data to CIHI’s NACRS and DAD, we found that 74.1% of COPD patients had at least one ED visit and that 34.4% of COPD patients had at least one acute care hospitalization during the study period. Further, 16.2% of ED visits resulted in an acute care hospital admission. Of those hospitalized, the majority of COPD patients were discharged home (81.6%) and received timely follow-up in primary care (81.0% within 30 days). ConclusionStructured and linkable EMR data provides opportunities to examine the patient journey through the care continuum in an innovative way. Using structured EMR data from the Alliance, linked with CIHI’s NACRS and DAD databases, we were able to generate a cohort of patients with COPD, explore the complexities of their primary care encounters and follow them through the continuum of care, namely emergency department visits and hospitalizations. It is hoped that the partnership between the Alliance and CIHI will help drive future efforts on addressing the gap in comparable EMR data in Canada, and internationally.

Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology

BackgroundWe developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use by patients and practitioners that is designed to be appropriate for integration into primary care health information technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes significant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses data that are readily available in primary care settings, it supports targeting of interventions delivered as part of clinical practice that are aimed at risk reduction. MethodsWe used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients who were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a model that is to be integrated into the same context from which the data were derived. ResultsThe resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art discrimination (estimated AUROC 0.84) and good calibration (assessed visually.) The model relies only on information that is readily available in Canadian primary care settings, and hence is appropriate for integration into Canadian primary care health information technology. ConclusionsIf the contextual challenges arising when using primary care electronic medical record data are appropriately addressed, highly discriminative models for osteoarthritis risk may be constructed using only data commonly available in primary care. Because the models are constructed from data in the same setting where the model is to be applied, internal validation provides strong evidence that the resulting model will perform well in its intended application.

Relative Effectiveness of the Cell-Cultured Quadrivalent Influenza Vaccine Compared to Standard, Egg-derived Quadrivalent Influenza Vaccines in Preventing Influenza-like Illness in 2017-2018.

BackgroundInfluenza antigens may undergo adaptive mutations during egg-based vaccine production. In the 2017–2018 influenza season, quadrivalent, inactivated cell-derived influenza vaccine (ccIIV4) vaccine was produced using A(H3N2) seed virus propagated exclusively in cell culture, thus lacking egg adaptive changes. This United States study estimated relative vaccine effectiveness (rVE) of ccIIV4 vs egg-derived quadrivalent vaccines (egg-derived IIV4) for that season.MethodsVaccination, outcome, and covariate data were ascertained retrospectively from a electronic medical record (EMR) dataset and analyzed. The study cohort included patients ≥ 4 years of age. rVE was estimated against influenza-like illness (ILI) using diagnostic International Classification of Diseases, Ninth or Tenth Revision codes. The adjusted odds ratios used to derive rVE estimates were estimated from multivariable logistic regression models adjusted for age, sex, race/ethnicity, geographic region, and health status.ResultsOverall, 92 187 individuals had a primary care EMR record of ccIIV4 and 1 261 675 had a record of egg-derived IIV4. In the ccIIV4 group, 1705 narrowly defined ILI events occurred, and 25 645 occurred in the standard egg-derived IIV4 group. Crude rVE was 9.2% (95% confidence interval [CI], 4.6%–13.6%). When adjusted for age, sex, health status, comorbidities, and geographic region, the estimated rVE changed to 36.2% (95% CI, 26.1%–44.9%).ConclusionsccIIV4, derived from A(H3N2) seed virus propagated exclusively in cell culture, was more effective than egg-derived IIV4 in preventing ILI during the 2017–2018 influenza season. This result suggests that cell-derived influenza vaccines may have greater effectiveness than standard egg-derived vaccines.

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

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. 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. 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%. 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.

Toward Standardized Monitoring of Patients With Chronic Diseases in Primary Care Using Electronic Medical Records: Development of a Tool by Adapted Delphi Procedure.

BackgroundLong-term care for patients with chronic diseases poses a huge challenge in primary care. There are deficits in care, especially regarding monitoring and creating structured follow-ups. Appropriate electronic medical records (EMR) could support this, but so far, no generic evidence-based template exists.ObjectiveThe aim of this study is to develop an evidence-based standardized, generic template that improves the monitoring of patients with chronic conditions in primary care by means of an EMR.MethodsWe used an adapted Delphi procedure to evaluate a structured set of evidence-based monitoring indicators for 5 highly prevalent chronic diseases (ie, diabetes mellitus type 2, asthma, arterial hypertension, chronic heart failure, and osteoarthritis). We assessed the indicators’ utility in practice and summarized them into a user-friendly layout.ResultsThis multistep procedure resulted in a monitoring tool consisting of condensed sets of indicators, which were divided into sublayers to maximize ergonomics. A cockpit serves as an overview of fixed goals and a set of procedures to facilitate disease management. An additional tab contains information on nondisease-specific indicators such as allergies and vital signs.ConclusionsOur generic template systematically integrates the existing scientific evidence for the standardized long-term monitoring of chronic conditions. It contains a user-friendly and clinically sensible layout. This template can improve the care for patients with chronic diseases when using EMRs in primary care.

Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms

BackgroundPrimary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients’ health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. This study was designed to test methods aimed at improving the quality of patient smoking information in a primary care EMR database.MethodsEMR data from community primary care settings extracted by two regional practice-based research networks in Alberta, Canada were used. Patients with at least one encounter in the previous 2 years (2016–2018) and having hypertension according to a validated definition were included (n = 48,377). Multiple imputation was tested under two different assumptions for missing data (smoking status is missing at random and missing not-at-random). A third method tested a novel pattern matching algorithm developed to augment smoking information in the primary care EMR database. External validity was examined by comparing the proportions of smoking categories generated in each method with a general population survey.ResultsAmong those with hypertension, 40.8% (n = 19,743) had either no smoking information recorded or it was not interpretable and considered missing. Those with missing smoking data differed statistically by demographics, clinical features, and type of EMR system used in the clinic. Both multiple imputation methods produced fully complete smoking status information, with the proportion of current smokers estimated at 25.3% (data missing at random) and 12.5% (data missing not-at-random). The pattern-matching algorithm classified 18.2% of patients as current smokers, similar to the population-based survey (18.9%), but still resulted in missing smoking information for 23.6% of patients. The algorithm was estimated to be 93.8% accurate overall, but varied by smoking status category.ConclusionMultiple imputation and algorithmic pattern-matching can be used to improve EMR data post-extraction but the recommended method depends on the purpose of secondary use (e.g. practice improvement or epidemiological analyses).

Blood Eosinophil Counts and Their Variability and Risk of Exacerbations in COPD: A Population-Based Study

BackgroundThere is controversy regarding the role of blood eosinophil levels as a biomarker of exacerbation risk in chronic obstructive pulmonary disease (COPD). Our aim was to quantify blood eosinophil levels and determine the risk of exacerbations associated with these levels and their variability. MethodsObservational, retrospective, population-based study with longitudinal follow-up in patients with COPD identified in a primary care electronic medical record database in Catalonia, Spain, covering 80% of the general population. Patients were classified into 4 groups using the following cut-offs: (a) <150cells/μl; (b) ≥150 and <300cells/μl; (c) ≥300 and <500cells/μl; (d) ≥500cells/μl. ResultsA total of 57,209 patients were identified with a mean age of 70.2 years, a mean FEV1(% predicted) of 64.1% and 51.6% had at least one exacerbation the previous year. The number of exacerbations in the previous year was higher in patients with the lowest and the highest eosinophil levels compared with the intermediate groups. During follow-up the number of exacerbations was slightly higher in the group with the lowest blood eosinophil levels and in those with higher variability in eosinophil counts, but ROC curves did not identify a reliable threshold of blood eosinophilia to discriminate an increased risk of exacerbations. ConclusionsOur results do not support the use of blood eosinophil count as a reliable biomarker of the risk of exacerbation in COPD in a predominantly non-exacerbating population. Of note was that the small group of patients with the highest variability in blood eosinophils more frequently presented exacerbations.

Diabetes prevalence and demographics in 25 First Nations communities in northwest Ontario (2014-2017).

First Nations communities are known to have high rates of diabetes. The rural First Nations communities in northwest (NW) Ontario are particularly affected. Regional studies in 1985 and 1994 found a high prevalence of diabetes. More recently, they are estimated to have the highest prevalence in Ontario at 19%, double the provincial norm. The purpose of this study is to examine the epidemiology and prevalence of diabetes in the total population and cardiovascular comorbidities in the adult population of 25 First Nations communities in NW Ontario. This retrospective diabetes prevalence study used primary care electronic medical record data for a 3-year period, 1 August 2014-31 July 2017. Diabetes prevalence was calculated for both the total and the adult (18+) populations and comorbid hypertension and dyslipidaemia were identified in adults. The age-adjusted diabetes prevalence for the total population was 15.1% versus a Canadian prevalence of 8.8%. The age-adjusted adult prevalence was 14.1%, double Canada's average of 7.1%. The average age of adults with diabetes was 52 years (±14.9); 57% were female. Comorbid hypertension (58%) and dyslipidaemia (73%) were common. Metformin was the most commonly used medication (58%), followed by insulin/analogues (23%) and sulphonylureas (13%). The diabetes prevalence in the First Nations population of NW Ontario is double Canada's norm. Addressing it will require addressing relevant social determinants of health, including poverty and food security.

Clinical risk groups and patient complexity: a case study with a primary care clinic in Alberta

Complexity and risk adjustment are two strategies employed to understand chronic disease and healthcare cost within patient populations. There is a general assumption that the data applied to risk adjustment models, such as the clinical risk groups (CRG) is sufficient to infer patient complexity. Our aim in this study was to compare the calculated complexity of a patient population using the 3 M™ CRG software to complexity data extracted from community-based primary care electronic medical records (EMR). We found that the distribution of the 3 M™ CRG health status was significantly different from the primary care EMR health status distribution, and that the number and type of chronic conditions identified differed between the two methods. We calculated a new variable that combined the information from the 3 M™ CRG software with the primary care EMR data. The distribution of the Combined-CRG distribution was significantly different from the 3 M™ CRG software; specifically, we saw many patients originally classified as being healthy or having minor chronic condition(s) re-categorized into having significant chronic condition(s). The CRG health statuses alone may be sufficient to predict future health expenditures, but caution is warranted if CRGs are to be used to infer complexity of the patient population.

A cross-sectional study examining convergent validity of a frailty index based on electronic medical records in a Canadian primary care program

BackgroundAn electronic frailty index (eFI) has been developed and validated in the UK; it uses data from primary care electronic medical records (EMR) for effective frailty case-finding in primary care. This project examined the convergent validity of the eFI from Canadian primary care EMR data with a validated frailty index based on comprehensive geriatric assessment (FI-CGA), in order to understand its potential use in the Canadian context.MethodsA cross-sectional validation study, using data from an integrated primary care research program for seniors living with frailty in Edmonton, AB. Eighty-five patients 65 years of age and older from six primary care physicians’ practices were recruited. Patients were excluded if they were under 65 years of age, did not provide consent to participate in the program, or were living in a long term care facility at the time of enrolment. We used scatter plots to assess linearity and Pearson correlation coefficients to examine correlations.ResultsResults indicate a strong statistically significant correlation between the eFI and FI-CGA (r = 0.72, 95% CI 0.60–0.81, p < 0.001). A simple linear regression showed good ability of the eFI scores to predict FI-CGA scores (F (1,83) = 89.06, p < .0001, R2 = 0.51). Both indices were also correlated with age, number of chronic conditions and number of medications.ConclusionsThe study findings support the convergent validity of the eFI, which further justifies implementation of a case-finding tool that uses routinely collected primary care data in the Canadian context.

Developing a case definition for type 1 diabetes mellitus in a primary care electronic medical record database: an exploratory study.

Identifying cases of disease in primary care electronic medical records (EMRs) is important for surveillance, research, quality improvement and clinical care. We aimed to develop and validate a case definition for type 1 diabetes mellitus using EMRs. For this exploratory study, we used EMR data from the Southern Alberta Primary Care Network within the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), for the period 2008 to 2016. For patients identified as having diabetes mellitus according to the existing CPCSSN case definition, we asked family physicians to confirm the diabetes subtype, to create the reference standard. We used 3 decision-tree classification algorithms and least absolute shrinkage and selection operator logistic regression to identify variables that correctly distinguished between type 1 and type 2 diabetes cases. We identified a total of 1309 people with type 1 or type 2 diabetes, 110 of whom were confirmed by their physicians as having type 1 diabetes. Two machine learning algorithms were useful in identifying these cases in the EMRs. The first algorithm used "type 1" text words or age less than 22 years at time of initial diabetes diagnosis; this algorithm had sensitivity 42.7% (95% confidence interval [CI] 33.5%-52.5%), specificity 99.3% (95% CI 98.6%-99.7%), positive predictive value 85.5% (95% CI 72.8%-93.1%) and negative predictive value 94.9% (95% CI 93.5%-96.1%). The second algorithm used a combination of free-text terms, insulin prescriptions and age; it had sensitivity 87.3% (95% CI 79.2%-92.6%), specificity 85.4% (95% CI 83.2%-87.3%), positive predictive value 35.6% (95% CI 29.9%-41.6%) and negative predictive value 98.6% (95% CI 97.7%-99.2%). We used machine learning to develop and validate 2 case definitions that achieve different goals in distinguishing between type 1 and type 2 diabetes in CPCSSN data. Further validation and testing with a larger and more diverse sample are recommended.

Prevalence of Hypertension, Treatment, and Blood Pressure Targets in Canada Associated With the 2017 American College of Cardiology and American Heart Association Blood Pressure Guidelines

The 2017 American College of Cardiology and American Heart Association (ACC/AHA) blood pressure (BP) guidelines redefined hypertension using a BP threshold of 130/80 mm Hg or greater and applied a treatment target of less than 130/80 mm Hg. To evaluate the potential change in the diagnosis, treatment, and control of hypertension in a Canadian cohort of patients with hypertension attending primary care practices using the ACC/AHA guidelines. This cross-sectional study used primary care practices across Canada electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network, extracted as of June 30, 2015. Adults with at least 1 primary care encounter in the previous 2 years (July 1, 2013, to June 30, 2015) were included in the study. Those with current hypertension were identified using a validated definition consisting of diagnoses, billing codes, and/or antihypertensive medication from within the primary care electronic medical record. Data analysis was conducted from December 2017 to July 2018. Proportion of individuals with a diagnosis of hypertension, prescribed antihypertensive medication, and meeting treatment BP targets. Of the 594 492 Canadian participants included in the study, 144 348 (24.2%) had hypertension (45.6% male; mean [SD] age, 65.5 [14.5] years). On applying the ACC/AHA guidelines, 252 279 individuals (42.4%) were considered hypertensive and half (51.0%; 95% CI, 50.8%-51.2%) were prescribed an antihypertensive medication. Individuals who were not previously considered to have hypertension but were reclassified as having elevated BP using the lower cutoff of 130/80 mm Hg or greater tended to be younger and were at lower cardiovascular risk. There was a shift toward more individuals requiring antihypertensive treatment, particularly in the lower-risk categories. The crude prevalence of hypertension increased from 13.3% to 32.0% in those aged 18 to 64 years, and of those aged 65 years and older, 16.6% more individuals were reclassified as having hypertension (from 55.2% to 71.8%). Only 12.3% of those who were considered at high risk were reclassified as hypertensive. Adoption of the ACC/AHA BP guidelines would result in a near doubling in the prevalence of hypertension in Canada. The changes would largely affect individuals who are younger and at low to moderate cardiovascular risk.

AMPUTATION RISK IN PATIENTS TREATED WITH ANTI-DIABETIC DRUGS INCLUDING SODIUM-GLUCOSE CO-TRANSPORTER-2 INHIBITOR AND INCRETINS: A COMPARATIVE REAL-WORLD OUTCOME STUDY

Recent studies in type 2 diabetes (T2D) patients reported contradictory results on the association of sodium-glucose co-transporter-2 inhibitor (SGLT2i) treatment with amputation risk. From nationally representative primary and ambulatory care Electronic Medical Records of UK and US, 3,060,562

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

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

Association between affective disorders presenting before and during pregnancy and pre-term birth, considering socio-demographic factors, obstetric factors, health conditions, and use of medication

ObjectiveTo study the relationship between maternal affective disorders (AD) before and during pregnancy, and pre-term birth. DesignRetrospective observational study. LocationSexual and reproductive health units at the Institut Català de la Salut (ICS) in Catalonia, Spain. ParticipantsPregnant women with a result of live-born child from 1/1/2012 to 30/10/2015. InterventionsData were obtained from the ICS Primary Care electronic medical record. Main measurementsDiagnosis of AD before and during pregnancy, months of pregnancy, and possible confusion factors were collected. Descriptive statistical analysis (median, interquartile range, and absolute and relative frequency), bivariate analysis (Wilcoxon test and Chi-square test), and multivariate analysis (logistic regression) were performed. Results102,086 women presented valid information for the study. Prevalence of AD during pregnancy was 3.5% (4.29% in pre-term and 3.46% in term births; p<0.004). Pregnant women with pre-term births presented a higher age, smoking habit, lower inter-pregnancy interval, and a lower socio-economic status. Pre-term birth was significantly associated to previous history of stress and dissociative disorder (SDD), anxiety, obsessive-compulsive disorder (OCD) and eating disorders (ED), and use of antidepressants. It was also associated to abuse of alcohol, smoking, and use of psychoactive substances, as well as SDD, ED, use of antipsychotics, and divorce during pregnancy. Multivariate analysis confirmed the relationship between pre-term birth and history of AD, SDD, ED, and smoking, but not with AD during pregnancy. ConclusionsExamining the previous history of SDD and ED in pregnant women, and SDD, and ED during pregnancy is highly relevant to avoid pre-term birth.

Challenges Associated with Cross-Jurisdictional Analyses using Administrative Health Data and Primary Care Electronic Medical Records in Canada.

Over the last 30 years, public investments in Canada and many other countries have created clinical and administrative health data repositories to support research on health and social services, population health and health policy. However, there is limited capacity to share and use data across jurisdictional boundaries, in part because of inefficient and cumbersome procedures to access these data and gain approval for their use in research. A lack of harmonization among variables and indicators makes it difficult to compare research among jurisdictions. These challenges affect the quality, scope, and impact of work that could be done. The purpose of this paper is to compare and contrast the data access procedures in three Canadian jurisdictions (Manitoba, Alberta and British Columbia), and to describe how we addressed the challenges presented by differences in data governance and architecture in a Canadian cross-jurisdictional research study. We characterize common stages in gaining access to administrative data among jurisdictions, including obtaining ethics approval, applying for data access from data custodians, and ensuring the extracted data is released to accredited individuals in secure data environments. We identify advantages of Manitoba’s flexible ‘stewardship’ model over the more restrictive ‘custodianship’ model in British Columbia, and highlight the importance of communication between analysts in each jurisdiction to compensate for differences in coding variables and poor quality data. Researchers and system planners must have access to and be able to make effective use of administrative health data to ensure that Canadians continue to have access to high-quality health care and benefit from effective health policies. The considerable benefits of collaborative population-based research that spans jurisdictional borders have been recognized by the Canadian Institutes for Health Research in their recent call for the creation of a National Data Platform to resolve many of the issues in harmonization and validation of administrative data elements.

Identify Patients with Congestive Heart Failure through Analyzing Free-Text Clinical Notes

IntroductionA number of challenges exist in analyzing unstructured free text data in electronic medical records (EMRs). EMR text are difficult to represent and model due to their high dimensionality, heterogeneity, sparsity, incompleteness, random errors and the presence of noise.&#x0D; Objectives and ApproachStandard Natural Language Processing (NLP) tools make errors when applied to clinical notes due to physician use of unconventional language, involving polysemy, abbreviations, ambiguity, misspelling, variations, and negation.&#x0D; This paper presents a novel NLP framework, “Clinical Learning On Natural Expression” (CLONE), to automatically learn from a large primary care EMR database, analyzing free text clinical notes from primary care practices. CLONE’s predictive clinical models using text mining and neural network approach to extract features to identify patterns. To demonstrate effectiveness, we evaluate CLONE’s ability in a case study to identify patients with a specific chronic condition: congestive heart failure (CHF).&#x0D; ResultsA random selected sample of 7500 patients from Electronic Medical Record Administrative data Linked Database (EMRALD) is used. In this dataset, each patient’s medical chart includes a reference standard, manually reviewed by medical practitioners. Prevalence of CHF is approximately 2%. The low prevalence leads to another challenging problem in machine learning: imbalanced datasets. After pre-processing, we build deep learning models to represent and extract important medical information from free text to identify CHF patients through analyzing patient charts. We evaluated the effectiveness of CLONE by comparing the predicted labels with the standard references on a holdout test dataset. Comparing it with a number of alternative algorithms, we improve the overall accuracy to over 90% on a test dataset.&#x0D; Conclusion/ImplicationsAs the role of NLP in EMR data expands, the CLONE natural language processing framework can lead to substantial reduction in manual processing, while improving predictive accuracy.