Aim To examine two methods of extracting risks for undetected type 2 diabetes (T2D): derived from electronic medical record (EMR) and family medicine (FM) assessment during pre-consultation phase. All risks were structured in three lists of patients’ data using Wonca International Classification Committee (WICC). Missing data were detected in each list. Methods A prospective study included a group of 1883 patients (aged 45-70) identified with risks. Risks were assessed based on EMR for continuity variables and FM’s assessment for episodes of disease and personal related information. Patients were categorized with final diagnostic test in normoglycaemia, impaired fasting glycaemia and undetected T2D. Results Total prevalence of diabetes was 10.9% (new 1.4%), of which 59.3% were females; mean age was 57.4. The EMR risks were hypertension in 1274 patients (yes 67.6%, no 27.9%, missing 4.4%), hypolipemic treatment in 690 (yes 36.6%, no 30.9%, miss 32.5%). In the episodes of disease: gestational diabetes mellitus in 31 women (yes 2.8%, missing 97.2%). Personal information: family history of diabetes in 649 (yes 34.5%, no 12.4%, missing 53.1%), overweight in 1412 (yes 75.0%, no 8.4%, missing 16.6%), giving birth to babies >4000g in 11 women (yes 0.9%, missing 99.1%). Overweight alone was the best predictor for undiagnosed type 2 diabetes, OR: 2.11 (CI: 1.41-3.15) (p<.001). Conclusion Two methods of extraction could not detect data for episodes of the disease. In the list of personal information, FMs could not assess overweight for one in six patients and family history for every other patient. The study can stimulate improving coded and structured data in EMR.
Khunti K, Mani H, Achana F, Cooper N, Gray L, Davies M. Systematic review and meta-analysis of response rates and diagnostic yield of screening for type diabetes and those at high risk of diabetes. Plos ONE. 2015;135702.
2.
International Diabetes Federation. IDF Diabetes Atlas. 6th ed. Brussels, Belgium: International Diabetes Federation. 2014;
3.
Standards of medical care in diabetes 2011. Diabetes Care. 2011;(Suppl 1):11–61.
4.
Van Den Donk M, Sandbeak A, Johnsen B, Lauritzen K, Simmons T, Wareham R, et al. Screening for type 2 diabetes. Lessons from the ADDITION Europe study. Diabet Med. 2011;1416–24.
5.
Evans P, Langley P, Gray D. Diagnosis type 2 diabetes before patients complain of diabetic symptomsclinical opportunistic screening in a single general practice. Fam Pract. 2008;376–81.
6.
Dankner R, Roth J. The personalized approach for detecting prediabetes and diabetes. Current Diabetes Reviews. 2016;58–65.
7.
Kuehlein T, Carvalho A, Viegas Dias C, Rodrigeus D, Pinto D. How do I care for my patients with. Journal of Health Science. 2015;141–7.
8.
Bergman M. Controversies and current approaches in the diagnosis of prediabetes and diabetes mellitus. Curr Diab Rev. 2016;1–6.
9.
Lee C, Colagiuri S. Population approaches for detecting glucose disorders. Curr Diab Rev. 2016;42–50.
10.
Williams T, Van Staa T, Puri S, Eaton S. Recent advances in the utility and use of the general practice research database as an example of a UK Primary Care Data resource. Ther Adv Drug Saf. 2012;89–99.
11.
Baričević Z, I, Botica V, Carkaxhiu M, L. New requirements of medical documentation in the area of chronic patients care in family medicine. Med Jad. 2014;39–43.
12.
Kuehlein T, Mennerat F, Kamenski G, Pinto D, Botica V, Van Boven M, et al. Poster Booklet of WHO-Family of International Classifications Network Annual Meeting. :11–7.
13.
Buijsse B, Simmons R, Griffin S, Schulze M. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011;46–62.
14.
Williamson D, Narayan K. Identification of persons with dysglycemia: terminology and practical significance. Prim Care Diabetes. 2009;211–7.
15.
Çevik B, A, Karaaslan M, Koçan M, Pekmezci S, H, et al. Prevalence and screening for risk factors of type 2 diabetes in Rize, Northeast Turkey: findings from a population-based study. Primary Care Diabetes. 2016;10–8.
16.
Woolthuis K, De Grauw E, Gerwen W, Hhoogen W, Lisdonk H, Metsemakers E, et al. Yield of opportunistic targeted screening for type 2 diabetes in primary care: the Diabscreen study. Ann Fam Med. 2009;422–30.
17.
Green L, Klinkman M. Perspectives in primary care: the foundational and urgent importance of a shared primary care data model. Ann Fam Pract. 2015;303–11.
18.
Laux G, Kuehlein T, Rosemann T, Szecsenyi J. Co and multimorbidity patterns in primary care based on episodes of care: results from the German CONTENT project. BMC Health Serv Res. 2008;14–24.
19.
Bryant J, Yoong S, Sanson-Fisher R, Mazza D, Carey M, Walsh J, et al. Is identification of smoking, risky alcohol consumption and overweight and obesity by general practitioners improving? A comparison over time. Fam Pract. 2015;664–71.
20.
Yoong L, Carey M, Sanson-Fisher R, Este D, Mackenzie C, Psyh L, et al. A cross-sectional study examining Australian general Practitioners? Identification of overweight and obese patients. J Gen Intern Med. 2014;328–34.
21.
Khoury M, Feero W, Valdez R. Family history and personal genomics as tools for improving health in era of evidence-based medicine. Am J Prev Med. 2010;184–8.
22.
Van Esch S, Heideman W, Cleijne W, Cornel M, Snoek F. Health care providers’ perspective on using family history in the prevention of type 2 diabetes: a qualitative study including different disciplines. BMC Fam Pract. 2013;31–9.
23.
Narayan K, Chan J, Viswanathan M. Early identification of type 2 Diabetes. Policy should be aligned with health systems strengthening. Diabetes Care. 2011;244–6.
24.
Sterne J, White I, Carlin J, Spratt M, Royston P, Kenward M, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;
25.
Dijana Haralović. :10.
26.
Katedra obiteljske medicine.
27.
Katedra obiteljske medicine, Sveučilište u Prištini.
28.
Katedra za medicinsku informatiku, Sveučilište u Zagrebu.
29.
Klinika, Zagreb, Zagreb, Hrvatska ; Edukacijski centar obiteljske medicine. Privatna praksa obiteljske medicine.
30.
Pho Medicinski I.
31.
Zdravlja Zagrebačke Županije D, Zagreb, Hrvatska ; Strukturirati rizike prema Wonca International Classification Comittee (WICC) i utvrditi koje rizike ne možemo otkriti.
32.
Metode U prospektivnoj studiji bilo je uključeno 1.883 pacijenta, dobi od 45 do 70 godina, s identificiranim rizicima za neotkrivenu ŠB-2. Rizici su otkriveni dvjema metodama: kontinuirane varijable iz zapisa EMR-a, rizike epizoda bolesti i personalne informacije o pacijentu prema procjeni FMs-a.
33.
Procjena liječnika o epizodama bolesti: gestacijski dijabetes kod 31 žene (“da” 2,8%, “nedostaju podaci” 97,2%).
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.