The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes

Primary Care Diabetes

Volumn 10, Issue 5, 2016, Pages 352-359

  Staff, Michael ^a   Roberts, Christopher ^b   March, Lyn ^b  

^a Northern Sydney Local Health District ^*  (Australia)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Computer modelling; Data completeness; Electronic records; Primary care

Indexed keywords

GLYCOSYLATED HEMOGLOBIN; HEMOGLOBIN A1C; HIGH DENSITY LIPOPROTEIN CHOLESTEROL; TRIACYLGLYCEROL; BIOLOGICAL MARKER; HEMOGLOBIN A1C PROTEIN, HUMAN;

ADULT; ARTICLE; BLOOD PRESSURE MEASUREMENT; CHOLESTEROL BLOOD LEVEL; CLINICAL OUTCOME; COMORBIDITY; COMPUTER MODEL; DIABETIC PATIENT; DISEASE DURATION; ELECTRONIC MEDICAL RECORD; HEMOGLOBIN BLOOD LEVEL; HUMAN; INSULIN TREATMENT; MAJOR CLINICAL STUDY; MIDDLE AGED; NON INSULIN DEPENDENT DIABETES MELLITUS; OUTCOME ASSESSMENT; OUTCOME VARIABLE; PRIMARY MEDICAL CARE; PRIORITY JOURNAL; SYSTOLIC BLOOD PRESSURE; TRIACYLGLYCEROL BLOOD LEVEL; AGED; AUSTRALIA; BIOLOGICAL MODEL; BLOOD; BLOOD PRESSURE; BODY HEIGHT; BODY WEIGHT; DATA MINING; ELECTRONIC HEALTH RECORD; FEMALE; HEALTH STATUS; INFORMATION PROCESSING; MALE; MEASUREMENT ACCURACY; MULTIVARIATE ANALYSIS; ODDS RATIO; PATHOPHYSIOLOGY; PRIMARY HEALTH CARE; STANDARDS; STATISTICAL MODEL; TIME FACTOR; TREATMENT OUTCOME;

AGED; AUSTRALIA; BIOMARKERS; BLOOD PRESSURE; BODY HEIGHT; BODY WEIGHT; CHOLESTEROL, HDL; DATA ACCURACY; DATA COLLECTION; DATA MINING; DIABETES MELLITUS, TYPE 2; ELECTRONIC HEALTH RECORDS; FEMALE; GLYCATED HEMOGLOBIN A; HEALTH STATUS; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; MULTIVARIATE ANALYSIS; ODDS RATIO; PATIENT-SPECIFIC MODELING; PRIMARY HEALTH CARE; TIME FACTORS; TREATMENT OUTCOME;

EID: 84961282518     PISSN: 17519918     EISSN: 18780210     Source Type: Journal    
DOI: 10.1016/j.pcd.2016.02.002     Document Type: Article

References (32)

1. [1] International Diabetes Federation, IDF Diabetes Atlas. sixth ed., 2013 ISBN: 2-930229-853. Available from http://www.idf.org/diabetesatlas (accessed 13.05.15).
2. [2] Gorter, K.J., Wens, J., Khunti, K., et al. The European EUCCLID pilot study on care and complications in an unselected sample of people with type 2 diabetes in primary care. Prim. Care Diabetes 4 (2010), 17–23.
3. [3] Department of Human Services (Australian Government), Practice Incentives Program. 2013 Available from http://www.humanservices.gov.au/spw/health-professionals/services/practice-incentives-programme/resources/diabetes-pip-guidelines.docx (accessed 20.10.15).
4. [4] Clinical Guidelines Taskforce (International Diabetes Federation), Global Guideline for Type 2 Diabetes. 2012 ISBN: 2-930229-43-8. Available from http://www.idf.org/global-guideline-type-2-diabetes-2012 (accessed 20.10.15).
5. [5] American Diabetes Association, Standards of Medical Care in Diabetes – 2015. Diabetes Care 38 (2015), S1–S94.
6. [6] Harris, M.F., Lloyd, J., Krastev, Y., et al. Routine use of clinical management guidelines in Australian general practice. Aust. J. Prim. Health 20 (2014), 41–46.
7. [7] Ford, D.R., Knight, A.W., The Australian Primary Care Collaboratives: an Australia general practice success story. Med. J. Aust. 193 (2010), 90–91.
8. [8] American Diabetes Association Consensus Panel, Guidelines for computer modeling in diabetes and its complications. Diabetes Care 27 (2004), 2262–2265.
9. [9] Palmer, A.J., Mount Hood 5 Modeling Group. Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting. Value Health 16 (2013), 670–685.
10. [10] Prescott, A., Bailey, J.E., Kelly, K.J., et al. The effectiveness and cost of single and multi-factorial cardiovascular risk factor modification to guideline targets in type 2 diabetes. Prim. Care Diabetes 6 (2012), 67–73.
11. [11] Hayes, A.J., Leal, J., Gray, A.M., et al. UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia 56 (2013), 1925–1933.
12. [12] Staff, M., Chen, J.S., March, L., Using computer modelled life expectancy to evaluate the impact of Australian Primary Care Incentive programs for patients with type 2 diabetes. Diabetes Res. Clin. Pract., 2015, 10.1016/j.diabres.2015.05.012 (in press).
13. [13] The Royal Australian College of General Practitioners and Diabetes Australia, General Practice Management of Type 2 Diabetes. 2014 ISBN: 978-0-86906-375-0 (web), ISBN: 978-086906-374-3 (print).
14. [14] Liaw, T.S., Taggart, J., Hairong, Y., de Lusignan, S., Data extraction from electronic health records – existing tools may be unreliable and potentially unsafe. Aust. Fam. Physician 42 (2013), 820–823.
15. [15] Peiris, D., Agaliotis, M., Patel, B., Patel, A., Validation of a general practice audit and extraction tool. Aust. Fam. Physician 42 (2013), 816–819.
16. [16] Köpcke, F., Trinczek, B., Majeed, R.W., et al. Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence. BMC Med. Inform. Decis. Mak., 13, 2013, 37 Available at http://www.biomedcentral.com/1472-6947/13/37.
17. [17] Staff, M., Can data extraction from general practitioners’ electronic records be used to predict clinical outcomes for patients with type 2 diabetes. Inform. Prim. Care 20 (2012), 95–102.
18. [18] Voorhamn, J., Denig, P., Computerized extraction of information on the quality of diabetes care from free text in electronic patient records of general practitioners. J. Am. Med. Inform. Assoc. 14 (2007), 349–354.
19. [19] De Clercq, E., Van Casteren, V., Jonckheer, P., et al. Electronic patient record data as proxy of GPs’ thoughts. Stud. Health Technol. Inform. 141 (2008), 103–110.
20. [20] de Lusignan, S., Wells, S.E., Hague, N.J., Thiru, K., Managers see the problems associated with coding clinical data as a technical issue whilst clinicians also see cultural barriers. Methods Inf. Med. 42 (2003), 416–422.
21. [21] Chazarda, E., Mouretb, C., Ficheura, G., et al. Proposal and evaluation of FASDIM, a fast and simple de-identification method for unstructured free-text clinical records. Int. J. Med. Inform. 83 (2014), 303–312.
22. [22] Rahimi, A., Liaw, S.T., Taggart, J., et al. Validating an ontology-based algorithm to identify patients with Type 2 Diabetes Mellitus in Electronic Health Records. Int. J. Med. Inform. 83 (2014), 768–778.
23. [23] Liaw, S.T., Rahimi, A., Ray, P., et al. Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature. Int. J. Med. Inform. 82 (2013), 10–24.
24. [24] Health Level Seven International, http://www.hl7.org/ (accessed 20.10.15).
25. [25] d'Emden, M.C., Shaw, J.E., Colman, P.G., et al. The role of HbA1c in the diagnosis of diabetes mellitus in Australia. Med. J. Aust. 197 (2012), 220–221.
26. [26] Allison, Paul D., Fixed Effects Regression Models. 2009, SAGE Publications Inc, California.
27. [27] StataCorp, Stata Statistical Software: Release 12. 2011, StataCorp LP, College Station, TX.
28. [28] Western, M.C., Dwan, K.M., Western, J.S., et al. Computerisation in Australian general practice. Aust. Fam. Physician 32 (2003), 180–185.
29. [29] McInnes, D.K., Saltman, D.C., Kidd, M.R., General practitioners’ use of computers for prescribing and electronic health records: results from a national survey. Med. J. Aust. 185 (2006), 88–91.
30. [30] Australian Institute of Health Welfare, Diabetes and disability: impairments, activity limitations, participation restrictions and comorbidities. Diabetes series no. 20. Cat. no. CVD 63. 2013, AIHW, Canberra.
31. [31] The Royal Australian College of General Practitioners, Guidelines for Preventive Activities in General Practice. eighth ed., 2012 978-0-86906-344-6 Available from http://www.racgp.org.au/ (accessed 20.10.15).
32. [32] Hippisley-Cox, J., Stables, D., Pringle, M., QRESEARCH: a new general practice database for research. Inform. Prim. Care(12), 2004, 498–550.