scholarly journals Wait times to rheumatology care for patients with rheumatic diseases: a data linkage study of primary care electronic medical records and administrative data

CMAJ Open ◽  
2016 ◽  
Vol 4 (2) ◽  
pp. E205-E212 ◽  
Author(s):  
J. Widdifield ◽  
S. Bernatsky ◽  
J. C. Thorne ◽  
C. Bombardier ◽  
R. L. Jaakkimainen ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Medical Records ◽  
Administrative Data ◽  
Rheumatic Diseases ◽  
Medical Records ◽  
Data Linkage ◽  
Linkage Study ◽  
Wait Times

scholarly journals Using Machine Learning to Measure Specialist Wait Times from Family Physicians’ Electronic Medical Records Linked to Ontario Health Administrative Data

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Mohamed Abdalla ◽  
Hong Lu ◽  
Bogdan Pinzaru ◽  
Liisa Jaakkimainen
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Electronic Medical Records ◽  
Administrative Data ◽  
Medical Records ◽  
Family Physicians ◽  
Wait Times ◽  
Free Text ◽  
Health Administrative Data ◽  
Specialist Physician

IntroductionReliable information about the time spent waiting for health care services is a critical metric for measuring health system performance. Wait times are a useful measure of access to various health care sectors. Alongside the increased adoption of electronic medical records (EMR) by Canadian family physicians (FP), is the secondary use of FP EMR data for research. However, using FP EMR data can be challenging in its unstructured, free-text format. Objectives and ApproachOur objective was to identify the target specialist physician type from the EMR FP referral note and then calculate wait times from a FP referral to a specialist physician visit. We used FP EMR data and linked to Ontario, Canada health administrative data (called EMRPC). EMRPC collects the entire clinical record from patients including the content of FP referral notes. We used machine learning (ML) methods to identify the type of specialist physician in which the referral was intended. Labels to test the ML methods were created from physicians’ claims data. Wait times were calculated from the FP EMR referral note date to the specialist physician claim date in administrative data. ResultsOur ML models’ ability to classify 2016 FP EMR referral notes to selected medical and surgical specialists achieved sensitivity and positive predictive values ranging from the high 70s to low 80s.Compared to earlier analyses from 2008, we observed a similar relative ordering to see specific specialist physicians. Overall, the median wait times have increased by 14 days on average, with a maximum increase of 28 days to see a gastroenterologist. Conclusion / ImplicationsThe accuracy of ML on unstructured FP EMR data is high, thereby providing a mechanism to “codifying” information in a timely manner. This information can help inform decision makers and providers about which patients or FP practices are experiencing long wait times in seeing specialist physicians.

908-P: Predictive Model for Glycemic Level of People with Diabetes Mellitus Using Data Extracted from Electronic Medical Records in Primary Care

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 908-P
Author(s):  
SOSTENES MISTRO ◽  
THALITA V.O. AGUIAR ◽  
VANESSA V. CERQUEIRA ◽  
KELLE O. SILVA ◽  
JOSÉ A. LOUZADO ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Primary Care ◽  
Predictive Model ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Glycemic Level ◽  
Using Data

Electronic medical records and primary care quality: Evidence from Manitoba

2021 ◽  
Vol 30 (5) ◽  
pp. 1124-1138
Author(s):  
Elisabet Rodriguez Llorian ◽  
Gregory Mason
Keyword(s):  
Primary Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Care Quality ◽  
Primary Care Quality ◽  
Quality Evidence

Asthma Electronic Medical Records in Primary Care: An Integrative Review

2010 ◽  
Vol 47 (8) ◽  
pp. 895-912 ◽  
Author(s):  
Janice P. Minard ◽  
Scott E. Turcotte ◽  
M. Diane Lougheed
Keyword(s):  
Primary Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Integrative Review

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

2018 ◽  
Vol 25 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Sarah Carsley ◽  
Catherine S. Birken ◽  
Patricia C. Parkin ◽  
Eleanor Pullenayegum ◽  
Karen Tu
Keyword(s):  
Primary Care ◽  
Body Mass Index ◽  
Electronic Medical Records ◽  
Body Mass ◽  
Medical Records ◽  
Data Access ◽  
World Health ◽  
Z Score ◽  
Data Completeness ◽  
Weight Data

BackgroundElectronic 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 standardisation of EMRs systems, data access and concerns about the quality of the data.ObjectivesThe 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.MethodsA cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Record Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the World Health Organization Growth Standards and Reference.ResultsA total of 54,964 children were identified from EMRALD. Overall, 93% had at least one complete set of growth measurements to calculate a body mass index (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.ConclusionsData 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.

scholarly journals 58 Overweight and obesity in children with autism spectrum disorder: Findings from primary care electronic medical records

2020 ◽  
Vol 25 (Supplement_2) ◽  
pp. e24-e24
Author(s):  
Laura M Kinlin ◽  
Sarah Carsley ◽  
Charles Keown-Stoneman ◽  
Natasha Saunders ◽  
Karen Tu ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Weight Status ◽  
Severe Obesity ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Overweight And Obesity ◽  
Typically Developing ◽  
Increased Risk

Abstract Introduction/Background Paediatric overweight and obesity are important public health problems worldwide. Children with autism spectrum disorder (ASD) may be at increased risk compared to their typically-developing peers; however, prevalence estimates in ASD have varied widely and existing studies have largely been limited by use of an external comparison group. Objectives To compare prevalence of overweight and obesity in children and youth (&lt;19 years of age) with and without ASD, using electronic medical record data from paediatric primary care visits. Design/Methods This was a cross-sectional analysis of EMRPC (Electronic Medical Records Primary Care) data, representing 385 family physicians in 43 clinics in Ontario, Canada. Age- and sex-standardized body mass index (BMI) z-scores were calculated using abstracted heights and weights from the most recent visit between January 2011 and December 2015. Weight status was determined using World Health Organization growth reference standards. ASD was defined using a previously-validated algorithm in EMRPC, based on an ASD-related term in the ‘Cumulative Patient Profile.’ Chi-square test statistics and multinomial logistic regression were used to compare weight status of those with and without ASD. Results In total, 44,625 children and youth were included, 632 [1.42%] with ASD. Distribution of weight status was significantly different between those with and without ASD (p&lt;0.001) [Table 1]. Compared to their typically-developing peers, children with ASD had significantly higher odds of overweight (unadjusted odds ratio [OR] 1.52; 95% confidence interval [CI] 1.24-1.87), obesity (unadjusted OR 2.55 (2.00-3.26) and severe obesity (unadjusted OR 3.09; 95% CI 2.08-4.60); these associations persisted after adjusting for sex, age, neighborhood income quintile and rural residence (Table 2). Conclusion Data from a large primary care database suggest that children with ASD are at substantially increased risk of overweight, obesity and severe obesity. Findings support the need for anticipatory guidance, prevention and management strategies specific to this clinical population. Future work will aim to better understand at what age differences in weight status emerge, and what nutritional, behavioural, or medical factors differentially affect weight status in the ASD population.

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