131 - Identifying Type 1 Diabetes in Electronic Medical Records and Administrative Data in Ontario

Purpose: To determine associations between physical activity (PA) and sport participation on HbA1c levels in children with type 1 diabetes (T1D). Method: Pediatric patients with T1D were invited to complete a PA and sport participation survey. Data were linked to their medical records for demographic characteristics, diabetes treatment and monitoring plans, and HbA1c levels. Results: Participants consisted of 71 females and 81 males, were 13 ± 3 years old with an average HbA1c level of 8.75 ± 1.81. Children accumulating 60 min of activity 3 days or more a week had significantly lower HbA1c compared to those who accumulated less than 3 days (p < 0.01) of 60 min of activity. However, there was no significant difference in HbA1c values based on sport participation groups. A multiple linear regression model indicated that PA, race, age, duration of diagnosis, and CGM use all significantly predicted HbA1c (p < 0.05). Conclusion: This study demonstrated the significant relationship between daily PA and HbA1c. Those in this sample presented with lower HbA1c values even if accumulating less than the recommended number of days of activity. Further, it was shown that sport participation alone may not be adequate enough to impact HbA1c in a similar manner.

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Enhancing Health Surveillance: Validation of a Novel Electronic Medical Records-Based Definition of Cases of Pediatric Type 1 and Type 2 Diabetes Mellitus

Canadian Journal of Diabetes ◽

10.1016/j.jcjd.2019.02.005 ◽

2019 ◽

Vol 43 (6) ◽

pp. 392-398

Author(s):

Leanne Kosowan ◽

Brandy Wicklow ◽

John Queenan ◽

Roseanne Yeung ◽

Shazhan Amed ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Electronic Medical Records ◽

Medical Records ◽

Health Surveillance ◽

Definition Of

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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

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2020-001224 ◽

2020 ◽

Vol 8 (1) ◽

pp. e001224

Author(s):

Alanna Weisman ◽

Karen Tu ◽

Jacqueline Young ◽

Matthew Kumar ◽

Peter C Austin ◽

...

Keyword(s):

Primary Care ◽

Type 1 Diabetes ◽

Administrative Data ◽

Predictive Value ◽

Incidence Rates ◽

Diabetes Clinic ◽

Healthcare Data ◽

Minimum Estimate ◽

Maximum Estimate

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 from Ontario, Canada, and to estimate T1D prevalence and incidence.Research design and methodsThe reference population was a random sample of patients with diabetes in EMRPC whose charts were manually abstracted (n=5402). Algorithms were developed using classification trees, random forests, and rule-based methods, using electronic medical record (EMR) data, administrative data, or both. Algorithm performance was assessed in EMRPC. Administrative data algorithms were additionally evaluated using a diabetes clinic registry with endocrinologist-assigned diabetes type (n=29 371). Three algorithms were applied to the Ontario population to evaluate the minimum, moderate and maximum estimates of T1D prevalence and incidence rates between 2010 and 2017, and trends were analyzed using negative binomial regressions.ResultsOf 5402 individuals with diabetes in EMRPC, 195 had T1D. Sensitivity, specificity, positive predictive value and negative predictive value for the best performing algorithms were 80.6% (75.9–87.2), 99.8% (99.7–100), 94.9% (92.3–98.7), and 99.3% (99.1–99.5) for EMR, 51.3% (44.0–58.5), 99.5% (99.3–99.7), 79.4% (71.2–86.1), and 98.2% (97.8–98.5) for administrative data, and 87.2% (81.7–91.5), 99.9% (99.7–100), 96.6% (92.7–98.7) and 99.5% (99.3–99.7) for combined EMR and administrative data. Administrative data algorithms had similar sensitivity and specificity in the diabetes clinic registry. Of 11 499 711 adults in Ontario in 2017, there were 24 789 (0.22%, minimum estimate) to 102 140 (0.89%, maximum estimate) with T1D. Between 2010 and 2017, the age-standardized and sex-standardized prevalence rates per 1000 person-years increased (minimum estimate 1.7 to 2.56, maximum estimate 7.48 to 9.86, p<0.0001). In contrast, incidence rates decreased (minimum estimate 0.1 to 0.04, maximum estimate 0.47 to 0.09, p<0.0001).ConclusionsPrimary care EMR and administrative data algorithms performed well in identifying T1D and demonstrated increasing T1D prevalence in Ontario. These algorithms may permit the development of large, population-based cohort studies of T1D.

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Autoimmune Diseases in Children and Adults With Type 1 Diabetes From the T1D Exchange Clinic Registry

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2016-2478 ◽

2016 ◽

Vol 101 (12) ◽

pp. 4931-4937 ◽

Cited By ~ 35

Author(s):

Jing W. Hughes ◽

Tonya D. Riddlesworth ◽

Linda A. DiMeglio ◽

Kellee M. Miller ◽

Michael R. Rickels ◽

...

Keyword(s):

Type 1 Diabetes ◽

Autoimmune Diseases ◽

Medical Records ◽

Age Groups ◽

Vascular Diseases ◽

Older Individuals ◽

Collagen Vascular Diseases ◽

Race Ethnicity ◽

Hispanic White

Background and Aims: Type 1 diabetes (T1D) is associated with other autoimmune diseases (AIDs), but the prevalence and associated predictive factors for these comorbidities of T1D across all age groups have not been fully characterized. Materials and Methods: Data obtained from 25 759 participants with T1D enrolled in the T1D Exchange Registry were used to analyze the types and frequency of AIDs as well as their relationships to gender, age, and race/ethnicity. Diagnoses of autoimmune diseases, represented as ordinal categories (0, 1, 2, 3, or more AIDs) were obtained from medical records of Exchange Registry participants. Results: Among the 25 759 T1D Exchange participants, 50% were female, 82% non-Hispanic white, mean age was 23.0 ± 16.9 years and mean duration of diabetes was 11 years. Of these participants, 6876 (27%) were diagnosed with at least one AID. Frequency of two or more AIDs increased from 4.3% in participants aged younger than 13 years to 10.4% in those aged 50 years or older. The most common AIDs were thyroid (6097, 24%), gastrointestinal (1530, 6%), and collagen vascular diseases (432, 2%). Addison’s disease was rare (75, 0.3%). The prevalence of one or more AIDs was increased in females and non-Hispanic whites and with older age. Conclusions: In the T1D Exchange Clinic Registry, a diagnosis of one or more AIDs in addition to T1D is common, particularly in women, non-Hispanic whites, and older individuals. Results of this study have implications for both primary care and endocrine practice and will allow clinicians to better anticipate and manage the additional AIDs that develop in patients with T1D.

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Agreement between Self-Reports and Medical Records in the Reporting of Pregnancy Outcomes in Women with Type 1 Diabetes

Research Journal of Women s Health ◽

10.7243/2054-9865-4-6 ◽

2017 ◽

Vol 4 (1) ◽

pp. 6

Author(s):

Andrea Rodgers Fischl ◽

Denise Charron-Prochownik ◽

Dorothy Becker ◽

William H Herman ◽

Laura McEwen ◽

...

Keyword(s):

Type 1 Diabetes ◽

Pregnancy Outcomes ◽

Medical Records ◽

Self Reports

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Preliminary Development and Evaluation of an Algorithm to Identify Breast Cancer Chemotherapy Toxicities Using Electronic Medical Records and Administrative Data

Journal of Oncology Practice ◽

10.1200/jop.2013.001288 ◽

2015 ◽

Vol 11 (1) ◽

pp. e1-e8 ◽

Cited By ~ 7

Author(s):

Jeanne S. Mandelblatt ◽

Karl Huang ◽

Solomon B. Makgoeng ◽

Gheorghe Luta ◽

Jun X. Song ◽

...

Keyword(s):

Breast Cancer ◽

Health Care ◽

Cost Effectiveness ◽

Electronic Medical Records ◽

Administrative Data ◽

Cancer Chemotherapy ◽

Medical Records ◽

Community Practice ◽

Health Care Settings ◽

Breast Cancer Chemotherapy

If validated in other samples and health care settings, algorithms to capture toxicity could be useful in comparative and cost effectiveness evaluations of community practice–delivered treatment.

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Barriers to high quality coding of hospital chart information to administrative data: A qualitative study

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.39 ◽

2017 ◽

Vol 1 (1) ◽

Cited By ~ 1

Author(s):

Karen Tang ◽

Kelsey Lucyk ◽

Hude Quan

Keyword(s):

Qualitative Study ◽

Electronic Medical Records ◽

Administrative Data ◽

Medical Records ◽

Health Service Delivery ◽

Structured Interview ◽

Recruitment Strategy ◽

Physician Education ◽

High Quality ◽

Patient Journey

ABSTRACTObjectives Administrative data are widely used in research, health policy, and the evaluation of health service delivery. We undertook a qualitative study to explore the barriers to high quality coding of chart information to administrative data, at the level of coders in Canada. ApproachOur study design is qualitative. We recruited professional medical chart coders and data users working across Alberta, Canada, using a multimodal recruitment strategy. We conducted an in-depth, semi-structured interview with each participant. All interviews were audio-recorded and transcribed. We conducted thematic analysis (e.g., line-by-line open coding) of interview transcripts. Codes were then collated into themes and compared across our dataset to ensure accurate interpretations of the data. The study team met to discuss, modify, and interpret emergent themes in the context of the barriers to coding administrative data. ResultsWe recruited 28 coding specialists. In general, coders had high job satisfaction and sense of collegiality, as well as sufficient resources to address their coding questions. They believed themselves to be adequately trained and consistently put in the extra effort when searching charts to find additional information that accurately reflected the patient journey. Barriers to high quality coding from the coder perspective included: 1) Incomplete and inaccurate information in physician progress notes and discharge summaries; 2) Difficulty navigating a complex hybrid of paper and electronic medical records; 3) Focus on productivity rather than quality by the employer, which at times resulted in inconsistent instructions for coding secondary diagnoses and discordant expectations between the employer and the coders’ professional standards. ConclusionFuture interventions to improve the quality of administrative data should focus on physician education of necessary components in charting, evaluation of electronic medical records from the perspectives of those who play a key role in abstracting data, and evaluation of productivity guidelines for coders and their effects on data quality.

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