Are family physicians comprehensively using electronic medical records such that the data can be used for secondary purposes? A Canadian perspective

Abstract Background With the introduction and implementation of a variety of government programs and policies to encourage adoption of electronic medical records (EMRs), EMRs are being increasingly adopted in North America. We sought to evaluate the completeness of a variety of EMR fields to determine if family physicians were comprehensively using their EMRs and the suitability of use of the data for secondary purposes in Ontario, Canada. Methods We examined EMR data from a convenience sample of family physicians distributed throughout Ontario within the Electronic Medical Record Administrative data Linked Database (EMRALD) as extracted in the summer of 2012. We identified all physicians with at least one year of EMR use. Measures were developed and rates of physician documentation of clinical encounters, electronic prescriptions, laboratory tests, blood pressure and weight, referrals, consultation letters, and all fields in the cumulative patient profile were calculated as a function of physician and patient time since starting on the EMR. Results Of the 167 physicians with at least one year of EMR use, we identified 186,237 patients. Overall, the fields with the highest level of completeness were for visit documentations and prescriptions (>70 %). Improvements were observed with increasing trends of completeness overtime for almost all EMR fields according to increasing physician time on EMR. Assessment of the influence of patient time on EMR demonstrated an increasing likelihood of the population of EMR fields overtime, with the largest improvements occurring between the first and second years. Conclusions All of the data fields examined appear to be reasonably complete within the first year of adoption with the biggest increase occurring the first to second year. Using all of the basic functions of the EMR appears to be occurring in the current environment of EMR adoption in Ontario. Thus the data appears to be suitable for secondary use

Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care

Background: Electronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However the use of EMRs in research has been impeded by lack of standardization of EMRs systems, data access and concerns about the quality of the data. Objectives: The study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes. Methods: A cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Records Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the WHO Growth Standards and Reference. Results: A total of 54,964 children were identified from EMRALD. Overall, 93% had at least 1 complete set of growth measurements to calculate a BMI z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values. Conclusions: Data completeness at well-child visits and overall data accuracy were greater than 90%. EMRs may be a valid source of data to provide estimates of obesity in children who attend primary care

Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care

Background: Electronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However the use of EMRs in research has been impeded by lack of standardization of EMRs systems, data access and concerns about the quality of the data.Objectives: The study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes.Methods: A cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Records Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the WHO Growth Standards and Reference.Results: A total of 54,964 children were identified from EMRALD. Overall, 93% had at least 1 complete set of growth measurements to calculate a BMI z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values.Conclusions: Data completeness at well-child visits and overall data accuracy were greater than 90%. EMRs may be a valid source of data to provide estimates of obesity in children who attend primary care

Data completeness in healthcare: A literature survey.

As the adoption of eHealth has made it easier to access and aggregate healthcare data, there has been growing application for clinical decisions, health services planning, and public health monitoring with daily collected data in clinical care. Reliable data quality is a precursor of the aforementioned tasks. There is a body of research on data quality in healthcare, however, a clear picture of data completeness in this field is missing. This research aims to identify and classify current research themes related to data completeness in healthcare. In addition, the paper presents problems with data completeness in the reviewed literature and identifies methods that have been adopted to address those problems. This study has reviewed 24 papers (January 2011–April 2016) published in information and computing sciences, biomedical engineering, and medicine and health sciences journals. The paper uncovers three main research themes, including design and development, evaluation, and determinants. In conclusion, this paper improves our understanding of the current state of the art of data completeness in healthcare records and indicates future research directions.N

Prognostic Predictive Model to Estimate the Risk of Multiple Chronic Diseases: Constructing Copulas Using Electronic Medical Record Data

Introduction: Multimorbidity, the presence of two or more chronic diseases in an individual, is a pressing medical condition. Novel prevention methods are required to reduce the incidence of multimorbidity. Prognostic predictive models estimate a patient’s risk of developing chronic disease. This thesis developed a single predictive model for three diseases associated with multimorbidity: diabetes, hypertension, and osteoarthritis. Methods: Univariate logistic regression models were constructed, followed by an analysis of the dependence that existed using copulas. All analyses were based on data from the Canadian Primary Care Sentinel Surveillance Network. Results: All univariate models were highly predictive, as demonstrated by their discrimination and calibration. Copula models revealed the dependence between each disease pair. Discussion: By estimating the risk of multiple chronic diseases, prognostic predictive models may enable the prevention of chronic disease through identification of high-risk individuals or delivery of individualized risk assessments to inform patient and health care provider decision-making

Successful Strategies for Adopting Electronic Medical Records Systems at Hospitals

Some healthcare leaders lack strategies to successfully implement electronic medical record (EMR) systems to improve patient care efficiency and effectiveness. Grounded in the technology acceptance model, the purpose of this qualitative multiple case study was to explore strategies healthcare leaders use to implement EMR systems. The participants comprised five healthcare leaders who implemented EMRs in U.S. hospitals. Data were collected using semistructured interviews, government websites, and relevant documents and analyzed using Yin’s five-step data analysis method. Three themes emerged: (a) adequate training, (b) workflow analysis, and (c) technical support. A key recommendation is for management to provide customized training to the healthcare employees based on employees’ verified roles and responsibilities within the EMR system. The implications for positive social change include the potential to provide physicians a platform for sharing patient data for customizing and improving patient care at reduced costs

Multimorbidity Among Adult Primary Health Care Patients In Canada: Examining Multiple Chronic Diseases Using An Electronic Medical Record Database

Introduction: The coexistence of multiple chronic diseases within an individual, also known as multimorbidity, is an ongoing challenge for patients, caregivers and primary health care (PHC) providers. An enhanced understanding of the burden of multimorbidity in Canada is needed. Objectives: This research had two main objectives. Objective One aimed to understand the prevalence of multimorbidity among adult PHC patients, as well as the patterns of unordered and ordered clusters of multiple chronic diseases. Objective Two aimed to determine the natural progression of multimorbidity over time, as well as the patient-, provider- and practice-level predictors of progressing into more complex clinical profiles. Methods: Data were derived from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) electronic medical record (EMR) database. For Objective One, descriptive and computational analyses were conducted and for Objective Two, multilevel survival analyses were conducted to account for clustering. Patients with at least one encounter recorded in their EMR and who were at least 18 years of age at their first encounter were included in the analyses. Chronic disease diagnoses were identified using the International Classification of Diseases, 9th Revision (ICD-9) and a list of 20 chronic disease categories identified patients with multimorbidity. Results: Overall, 53.3% and 33.1% of adult PHC patients were living with at least two and at least three chronic diseases, respectively. Patients with at least two chronic diseases had a mean age of 59.0 years (SD: 17.0), while the majority were female (57.8%) and living in an urban setting (52.2%). Among female patients with multimorbidity, 6,095 unique combinations and 14,911 unique permutations were found. Among male patients with multimorbidity, 4,316 unique combinations and 9,736 unique permutations were detected. The multilevel survival analysis indicated that several patient-level (patient age, patient sex and total number of chronic diseases), provider-level (provider age) and practice-level (EMR type and practice location) variables predicted time until subsequent chronic disease diagnoses. Conclusion: This research explored the prevalence, patterns and natural progression of multimorbidity over time among a large cohort of adult PHC patients. When carefully assessed, these findings will help to create a more nuanced understanding of the burden of multimorbidity