Yearb Med Inform 2017; 26(01): 201-208
DOI: 10.15265/IY-2017-005
Section 9: Clinical Research Informatics
Working Group Contribution
Georg Thieme Verlag KG Stuttgart

Measuring Quality of Healthcare Outcomes in Type 2 Diabetes from Routine Data: a Seven-nation Survey Conducted by the IMIA Primary Health Care Working Group

Primary Health Care Informatics Working Group Contribution to the Year Book of Medical Informatics 2017
W. Hinton
1   Clinical Informatics & Health Outcomes Research Group, Department of Clinical & Experimental Medicine, University of Surrey, Guildford, Surrey, UK
,
H. Liyanage
1   Clinical Informatics & Health Outcomes Research Group, Department of Clinical & Experimental Medicine, University of Surrey, Guildford, Surrey, UK
,
A. McGovern
1   Clinical Informatics & Health Outcomes Research Group, Department of Clinical & Experimental Medicine, University of Surrey, Guildford, Surrey, UK
,
S.-T. Liaw
2   School of Public Health & Community Medicine, UNSW Medicine, Australia
,
C. Kuziemsky
3   Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada
,
N. Munro
1   Clinical Informatics & Health Outcomes Research Group, Department of Clinical & Experimental Medicine, University of Surrey, Guildford, Surrey, UK
,
S. de Lusignan
1   Clinical Informatics & Health Outcomes Research Group, Department of Clinical & Experimental Medicine, University of Surrey, Guildford, Surrey, UK
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Weitere Informationen

Publikationsverlauf

Publikationsdatum:
11. September 2017 (online)

Summary

Background: The Institute of Medicine framework defines six dimensions of quality for healthcare systems: (1) safety, (2) effectiveness, (3) patient centeredness, (4) timeliness of care, (5) efficiency, and (6) equity. Large health datasets provide an opportunity to assess quality in these areas.

Objective: To perform an international comparison of the measurability of the delivery of these aims, in people with type 2 diabetes mellitus (T2DM) from large datasets.

Method: We conducted a survey to assess healthcare outcomes data quality of existing databases and disseminated this through professional networks. We examined the data sources used to collect the data, frequency of data uploads, and data types used for identifying people with T2DM. We compared data completeness across the six areas of healthcare quality, using selected measures pertinent to T2DM management.

Results: We received 14 responses from seven countries (Australia, Canada, Italy, the Netherlands, Norway, Portugal, Turkey and the UK). Most databases reported frequent data uploads and would be capable of near real time analysis of healthcare quality.

The majority of recorded data related to safety (particularly medication adverse events) and treatment efficacy (glycaemic control and microvascular disease). Data potentially measuring equity was less well recorded. Recording levels were lowest for patient-centred care, timeliness of care, and system efficiency, with the majority of databases containing no data in these areas. Databases using primary care sources had higher data quality across all areas measured.

Conclusion: Data quality could be improved particularly in the areas of patient-centred care, timeliness, and efficiency. Primary care derived datasets may be most suited to healthcare quality assessment.

 
  • References

  • 1 Majeed A, Car J, Sheikh A. Accuracy and completeness of electronic patient records in primary care. Fam Pract 2008; 25 (04) 213-4.
  • 2 Clay RA. The advantages of electronic health records. Electronic records can improve patient care. Here’s how. Available from: http://www.apa.org/monitor/2012/05/electronic-records.aspx [Accessed 12th December 2016].
  • 3 Institute of Medicine (IOM). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C: National Academy Press; 2001
  • 4 de Lusignan S, van Weel C. The use of routinely collected computer data for research in primary care: opportunities and challenges. Fam Pract 2006; Apr; 23 (02) 253-63.
  • 5 The diabetes pandemic. Lancet 2011; Jul 9; 378 (9786): 99.
  • 6 Liyanage H, Liaw ST, Kuziemsky C, Terry AL, Jones S, Soler JK. et al. The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review. Contribution of the IMIA Primary Health Care Informatics WG. Yearb Med Inform 2013; 147-54.
  • 7 de Lusignan S, Liaw ST, Krause P, Curcin V, Vicente M, Michalakidis G. et al. Key concepts to assess the readiness of data for International research: Data quality, lineage and provenance, extraction and processing errors, traceability, and curation. Yearb Med Inform 2011; 112-21.
  • 8 de Lusignan S, Liaw S, Michalakidis G, Jones S. Defining data sets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology driven approach. Inform Prim Care 2011; 19 (03) 127-34.
  • 9 Liaw S, Rahimi A, Ray P, Taggart J, Dennis S, de Lusignan S. et al. Towards an ontology for data quality in integrated chronic disease: a realist review of the literature. Int J Med Inform 2013; 82 (01) 10-24.
  • 10 Taggart J, Liaw ST, Dennis S, Yu H, Rahimi A, Jalaludin B. et al. The University of NSW electronic practice based research network: disease registers, data quality and utility. Stud Health Technol Inform 2012; 178: 219-27.
  • 11 Taggart J, Liaw S-T, Yu H. Structured data quality reports to improve EHR data quality. Int J Med Inform 2015; 84: 1094-8.
  • 12 Jonnagaddala J, Liaw S, Ray P, Kumar M, Chang N, Dai H. Coronary artery disease risk assessment from unstructured electronic health records using text mining. J Biomed Inform. 2015 58 S203-10.
  • 13 Jonnagaddala J, Liaw S, Ray P, Kumar M, Dai H, Hsu C. Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records. Biomed Res Int 2015; 2015: 636371.
  • 14 Liaw S, Taggart J, Yu H, de Lusignan S, Kuziemsky C, Hayen A. Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease registers. J Biomed Inform 2014; 52: 364-72.
  • 15 Rahimi A, Liaw S, Ray P, Taggart J, Yu H. Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review. Decision Analytics 2014; 01 (01) 5.
  • 16 Stratton IM. et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 2000; Aug 12; 321 (7258): 405-12.
  • 7 Kohner EM. Microvascular disease: what does the UKPDS tell us about diabetic retinopathy?. Diabet Med 2008; 25 (Suppl. 02) 20-4.
  • 18 Goodman ALeo. Snowball Sampling. Annals of Mathematical Statistics 1961; 32: 148-70.
  • 19 Goudswaard AN, Lam K, Stolk RP, Rutten GE. Quality of recording of data from patients with type 2 diabetes is not a valid indicator of quality of care. A cross-sectional study. Fam Pract 2003; Apr; 20 (02) 173-7.
  • 20 Keating NL, Landrum MB, Landon BE, Ayanian JZ, Borbas C, Guadagnoli E. Measuring the quality of diabetes care using administrative data: is there bias?. Health Serv Res 2003; Dec; 38 (6 Pt 1): 1529-45.
  • 21 Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML. et al. A comparison of phenotype definitions for diabetes mellitus. J Am Med Inform Assoc 2013; 20: e319-e326.
  • 22 McGovern A, Hinton W, Correa A, Munro N, Whyte M, de Lusignan S. Real-world evidence studies into treatment adherence, thresholds for intervention and disparities in treatment in people with type 2 diabetes in the UK. BMJ Open 2016; Nov 24; 06 (11) e012801.
  • 23 Correa A, Hinton W, McGovern A, van Vlymen J, Yonova I, Jones S, de Lusignan S. et al. Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) sentinel network: a cohort profile. BMJ Open 2016; Apr 20; 06 (04) e011092.
  • 24 Miller DR, Pogach L. Longitudinal approaches to evaluate health care quality and outcomes: the Veterans Health Administration diabetes epidemiology cohorts. J Diabetes Sci Technol 2008; Jan; 02 (01) 24-32.
  • 25 Hill F, Bradley C. A computer based, automated analysis of process and outcomes of diabetic care in 23 GP practices. Ir Med J 2012; Feb; 105 (02) 45-7.
  • 26 Bailie R, Bailie J, Chakraborty A, Swift K. Consistency of denominator data in electronic health records in Australian primary healthcare services: enhancing data quality. Aust J Prim Health 2015; 21 (04) 450-9.
  • 27 Turnbull PJ, Sinclair AJ. Evaluation of nutritional status and its relationship with functional status in older citizens with diabetes mellitus using the mini nutritional assessment (MNA) tool--a preliminary investigation. J Nutr Health Aging 2002; May; 06 (03) 185-9.
  • 28 Engel G. The need for a new medical model: a challenge for biomedicine. Science 1977; 196 4286 129-36.
  • 29 Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M. et al. Management of Hyperglycemia in Type 2 Diabetes: A Patient-Centered Approach. Position Statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 2012; 35 (06) 1364-79.
  • 30 American Diabetes Association. ADA: Standards of Medical Care in Diabetes 2016. Diabetes Care 2016; 39 (S1): S1-S112.
  • 31 NICE. Type 2 diabetes in adults: management. NICE guidelines [NG28]2015. Available from: https://www.nice.org.uk/guidance/ng28 [Accessed 12th December 2016].
  • 32 Shafiee G, Mohajeri-Tehrani M, Pajouhi M, Larijani B. The importance of hypoglycemia in diabetic patients. Journal of Diabetes & Metabolic Disorders 2012; 11: 17.